We welcome submitted talks 🎤 and posters 🖼️ !
Abstract submissions are closed.Abstract submissions are closed
we encourage unpublished and already published work alike
We are currently only considering in-person presentations
We welcome submissions for flash talks (~ 5 min), short talks (~25 min), and long talks (~40 min). If your abstract is accepted, we will communicate with you what length format works best, considering your submitted preferences and our requirements.
Posters are a great way to learn, share, and get feedback on your work in a helpful and friendly environment. The project does not have to be “done”, in fact, it’s probably more beneficial that your poster is on a work-in-progress project for something that you would like expert advice on. For posters, we also welcome submissions on a topic or interest that you are passionate about but may not have results on.
Take the opportunity. You can also win prizes 🏆!
Please view this video for an example on how to improve your poster design https://www.youtube.com/watch?v=1RwJbhkCA58 [adjust the template to your own needs!]
This form includes space to submit your research for a talk and/or poster. You should have the following items prepared: (1) title (2) EITHER ~300 abstract OR an existing publication.
Accepted talks will have their fees waived if applicable, but speakers are expected to make their own way to the Indaba𝕏. Accepted posters have the same fee. The Indaba𝕏 is NOT a “pay-to-speak” affair. Sponsors and speakers are independently organised, but can overlap.
Published work We welcome the submission of published work for a talk or poster.
Unpublished work Abstracts will be evaluated on the basis of a submitted abstract.
The submission should clearly explain why your question is important and how your claims will advance the field. Include enough detail that reviewers can assess the technical content of your methods and results. Please be sure to address the significance and fit of your submission for the audience, which includes a mix of academics and industry interested in the functional properties of deep learning and machine learning.